Person:

Goldfarb-Rumyantzev, Alexander

Introduction: Definitions of underprivileged status based on race, gender and geographic location are neither sensitive nor specific; instead we proposed and validated a composite index of social adaptability (SAI). Material and methods: Index of social adaptability was calculated based on employment, education, income, marital status, and substance abuse, each factor contributing from 0 to 3 points. Index of social adaptability was validated in NHANES-3 by association with all-cause and cause-specific mortality. Results: Weighted analysis of 19,593 subjects demonstrated mean SAI of 8.29 (95% CI 8.17-8.40). Index of social adaptability was higher in Whites, followed by Mexican-Americans and then the African-American population (ANOVA, p < 0.001). The SAI was higher in subjects living in metropolitan compared to rural areas (T-test, p < 0.001), and was greater in men than in women (T-test, p < 0.001). In Cox models adjusted for age, comorbidity index, BMI, race, sex, geographic location, hemoglobin, serum creatinine, albumin, cholesterol, and glycated hemoglobin levels, SAI was inversely associated with mortality (HR 0.87 per point, 95% CI 0.84-0.90, p < 0.001). This association was confirmed in subgroups. Conclusions: We proposed and validated an indicator of social adaptability with a strong association with mortality, which can be used to identify underprivileged populations at risk of death.

Introduction: Because chronic kidney disease (CKD) adversely affects survival, prediction of mortality risk should help to identify individuals requiring therapeutic intervention. The goal of this project was to construct and to validate a risk scoring system and prediction model of the probability of 2-year mortality in a CKD population. Methods: We applied the Woodpecker approach to develop prediction equations using linear, exponential, and combined models. A risk indicator R on a scale of 0 to 10 was calculated as follows: starting with 0, add 0.048 for each year of age above 20, 0.45 for male sex, 0.49 for each stage of CKD over stage 2, 1.04 for proteinuria, 0.72 for smoking history, and 0.49 for each significant comorbidity up to 5. Results: Using R to predict 2-year mortality, the model yielded an area under the receiver operating characterisic curve of 0.83 (95% confidence interval = 0.81−0.86) with 5062 subjects with CKD ≥stage 2 from a National Health and Nutrition Examination Survey cohort (1999−2004) having a 3.2% 2-year mortality. The combined expression offered results closest to most actual outcomes for the entire population and for each CKD stage. For those patients with higher risk (R ≥ 4−5, >5−6, and >6), the predicted 2-year mortality rates were 3.8%, 6.4%, and 13.0%, respectively, compared to observed mortality rates of 2.7%, 4.5%, and 13.3%. Conclusion: The risk stratification tool and prediction model of 2-year mortality demonstrated good performance and may be used in clinical practice to quantify the risk of death for individual patients with CKD.